Interplanetary communications network, interplanetary communications network backbone and method of managing interplanetary communications network

ABSTRACT

An interplanetary communications network, an interplanetary communications backbone network of Artificial Neural Network (ANN) nodes, an ANN node and a method of managing interplanetary communications. The backbone network operates as a neural network with each node identifying optimum paths, e.g., end-to-end through the backbone network from a distant planet to an earth node. Each node maintains a window matrix identifying reoccurring (e.g., periodically) communications windows between nodes and a propagation delay matrix identifying time varying propagation delays between nodes. Each node determines whether and how long to store packets locally to minimize path delays. Each node also maintains a link cost matrix indicating the cost of links to neighboring nodes and further determines whether and how long to store packets locally to minimize path delays at minimal link cost.

CROSS REFERENCE TO RELATED APPLICATION

The disclosure is related to U.S. patent application Ser. No.11/187,452, (Attorney Docket No. 024.0096 (04-1051)) entitled “TACTICALCOGNITIVE-BASED SIMULATION METHODS AND SYSTEMS FOR COMMUNICATION FAILUREMANAGEMENT IN AD-HOC WIRELESS NETWORKS,” to Hesham El-Damhougy, filedJul. 22, 2005; and to U.S. patent application Ser. No. 11/426,417,(Attorney Docket No. 05-0278) entitled “NEURAL NETWORK-BASED MOBILITYMANAGEMENT FOR MOBILE AD HOC RADIO NETWORKS,” U.S. patent applicationSer. No. 11/426,419, (Attorney Docket No. 05-1032) entitled “NEURALNETWORK-BASED NODE MOBILITY AND NETWORK CONNECTIVTY PREDECTIONS FORMOBILE AD HOC RADIO NETWORK,” U.S. patent application Ser. No.11/426,425, (Attorney Docket No. 05-1033) entitled “NEURAL NETWORK-BASEDMOBILITY MANAGEMENT FOR SELF-PARTITION DETECTION AND IDENTIFICATION OFMOBILE AD HOC RADIO NETWORKS,” and U.S. patent application Ser. No.11/426,428, (Attorney Docket No. 05-1034) entitled “NEURAL NETWORK-BASEDMOBILITY MANAGEMENT FOR HEALING MOBILE AD HOC RADIO NETWORKS,” to HeshamEl-Damhougy, all four filed Jun. 26, 2006, assigned to the assignee ofthe present application and incorporated herein by reference.

BACKGROUND

1. Technical Field

The embodiments of the disclosure generally relate to ad-hoccommunications networks and, more particularly, to an ad-hocinterplanetary communications network for adaptable deep-spacecommunications in an unstructured and self-supervised interplanetary orsub-planetary environment, such as between deep-space or lunar probesand earth.

2. Background Description

The National Air and Space Administration (NASA) is planning for futureouter space exploration and, especially, for deep space interplanetaryexploration in our solar system and beyond. Current plans allow for ayet-to-be-designed and developed interplanetary communications networkto provide communication services between space borne entities(including deep space entities) and the earth. Primarily, theinterplanetary communications network is expected to providecommunication services for scientific data delivery and also providenavigation services for exploration spacecraft and orbiters in futuredeep space missions. The current vision for the infrastructure of thisinterplanetary communications network is similar to the Internet. See,e.g., Akyildiz et al., “InterPlaNetary Internet: state-of-the-art andresearch challenges,” Computer Networks, 43 (2003). This as yetunrealized interplanetary communications network infrastructure or,Interplanetary Internet, is enabling networking technology for futuredeep space scientific exploration missions such as Mars and Neptuneexploration and beyond.

Generally, an interplanetary communications network is expected toinclude communication between nodes at various space borne entities orlocations, e.g., at fixed (celestially fixed) and/or mobilecommunications platforms. Individual nodes may include, for example,fixed (on a planet surface) sensors, and mobile nodes, e.g., robotics aswell as human operated nodes. The nodes are expected to be distributedat numerous space borne locations and deep space entities. Theseentities may include, for example, robotic spacecraft and CrewExploration Vehicles (CEV's); planetary platforms, e.g., orbital, localflight and surface planet (mobile and fixed) vehicles; and,sub-planetary probes, e.g., on moons, satellites, and asteroids.

Neither terrestrial Internet-based routing nor terrestrial mobile ad hocrouting protocols satisfy space communications parameter requirementsbecause of additional constraints and requirements for spacecommunications, such as burst data transfers between nodes in a shorttransfer window. A typical Earth-based wireless network includes fixedcommunications backbone nodes (e.g., base stations) that define cells,for example, connected together in the network. An earth network thatlacks the fixed communications backbone nodes is known as an ad-hocnetwork. Instead, a group of autonomous (and frequently mobile) nodesdefine the ad-hoc Earth network. However, since there is no fixed frameof reference in space, node locations are in constant motion withrespect to one another even at rest. Consequently, backbone networkstructure is expected to be fluid and continually, dynamically changing,whether as a result of planetary rotation or orbital movement.Dynamically changing node locations cause connectivity among the networknodes to vary with time. Further, connectivity may change because ofother interference, such as blockage of the line-of-sight communicationspath by a planet or from extra-network interference, e.g., sunspotactivity. This continual connectivity change makes networkinfrastructure time varying also and difficult to pre-define, especiallyas the total number of nodes gets large. Thus, the interplanetarycommunications network is expected to be an ad-hoc network, primarily ofautonomous nodes self-managing and self-maintaining connectivity inspite of the fluidity of the network communication paths.

These autonomous nodes must assure some form of network connectivity tomaintain end-to-end communications for mission success. This isespecially important for exchanging large volumes of data that may becollected by various space borne network platforms. Therefore, thenetwork nodes themselves must automaticallyself-configure/self-provision nodes/platforms along network paths todeliver the expected volume of data. Furthermore, this must be withminimal or no manual intervention/interference, as none may beavailable. Given that even when a communications window is availablebetween two nodes, there may still be a relatively long transmissionpath lag time or propagation delay, even between two relatively closecommunicating nodes, e.g., on the moon and on the Earth. Therefore,Akyildiz et al. describe several significant challenges and issues thatmust be addressed and resolved before interplanetary communicationsnetwork objectives may be realized.

Specifically, backbone layer routing is a serious problem area with keypreviously unresolved challenges. Traditional Shortest Path Algorithms(SPA) include, for example, the Bellman-Ford algorithm and Dijkstra'salgorithm. The Bellman-Ford algorithm has been realized by the knownInternet Border Gateway Protocol (BGP). Dijkstra's algorithm has beenrealized by the Internet Open Shortest Path First (OSPF) protocol forAutonomous Systems (AS). The interplanetary communications network willnot have a traditional end-to-end path because of long periods (minutes,hours or even days) of no connectivity between nodes and groups ofnodes. End-to-end connectivity is not guaranteed and, if it occurs, itmay be only sporadic. Therefore, traditional end-to-end routingapproaches are unsuitable for interplanetary communications networkrouting. Moreover, because of nodal motion, it may be difficult toidentify an end-to-end path because performance/routing metrics (e.g.,propagation and connectivity metrics) are time-dependent. Consequently,optimal or suboptimal routes are time-dependent. This time-dependencemakes both the Bellman-Ford algorithm and Dijkstra's algorithminadequate.

With current technology achieving significant distances in space, suchas interplanetary space travel, interplanetary missions currently takeyears to reach their objectives. Subsequently, distant nodes are likelyto be the oldest and have the oldest equipment. Consequently, storagethat increases in density with each new generation, for example, islikely to be denser and more plentiful at nodes closer to earth andscarcer at distant nodes. Thus, storage capacity may be in short supplyand, therefore, very costly at these distant nodes as well as otherintervening nodes in the network paths. As a result, long term storagerequirements for storing data when a connection is unavailable can causestorage contention and overflow at those distant or intervening nodes,e.g., from data arriving simultaneously from several distant nodes.Therefore, locating and planning an optimal route requires completeknowledge and consideration of network path resources as well as keytime-dependent network parameters, e.g., contact times and orbitalparameters, and traffic loads and node queuing delays.

Furthermore, an interplanetary communications network is likely to be anamalgamation of sub-networks that are based on different distinctnetwork protocols. These distinct network protocols must communicatewith the network through strategically located gateways. However,maintaining an even data traffic flow between network nodes that arebased on different distinct such network protocols requires that networkgateways seamlessly convert between network protocols. Though gatewaypositions are predictable, they are also normally time varying (e.g.,satellites orbiting about a distant planet). This variation in locationfurther complicates gateway selection and handover by network nodes,both locally to the gateway and in the network backbone.

Accordingly, there is a need for a self organizing interplanetarycommunications network for communicating between earth and explorationand data collecting probes, both manned and unmanned and, moreparticularly, for transporting mission critical data with minimum delayand data loss.

SUMMARY

An advantageous embodiment includes an interplanetary communicationsnetwork, an interplanetary communications backbone network utilizing anArtificial Neural Network (ANN) at each node. Each ANN at network nodesprovides an on-line real time estimation of node weights/metrics neededto compute the shortest path between two nodes of the interplanetarycommunications network (in particular the backbone network). Thebackbone network operates as an autonomous network with each nodeidentifying a path or paths through the backbone network from a distantplanet to an earth node. The identified paths may be end-to-end completeoptimum paths or an optimum next hop to the next node along an optimumend-to-end path. Each node maintains a window matrix identifyingcommunications windows between nodes and a propagation delay matrixidentifying time varying propagation delays between nodes. Each nodedetermines whether and how long to store packets locally to minimizepath delays. Each node also maintains a link cost matrix indicating thecost of links to neighboring nodes and further determines whether andhow long to store packets locally to minimize path delays at minimallink cost.

Advantageously, a backbone network in a preferred interplanetarycommunications network provides robust end-to-end network routing.Backbone routing may be accomplished by one of two preferred algorithmsthat are suitable for interplanetary network, time dependent link staterouting or time dependent hop-by-hop distance vector optimization. Fortime dependent link state routing a complete path is identified fromeach node to every other node in the network and may be based on amodified Dijkstra Algorithm. For time dependent hop-by-hop distancevector an optimum path is identified through delivery to the next hopalong the shortest path and may be based on a modified Bellman-Fordalgorithm. A preferred interplanetary backbone network uses either linkstate or distance vector routing schemes for on-line real time automaticdynamic routing. End-to-end transmission times are minimized and linkcost is dramatically reduced. Moreover, the backbone network isdynamically adaptable to network changes and, further, is immune to manynetwork partitions/node failures. Separating nodes may be identified inadvance and reconnection (i.e., communications windows) predictedaccording to orbital motion. Network stability is improved withpotential network communications and routing oscillation reduced or eveneliminated. Alternate routes are identified with relatively fastconvergence in both off-line and on-line real time implementations.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, aspects and advantages will be betterunderstood from the following detailed description of a preferredembodiment of the disclosure with reference to the drawings, in which:

FIGS. 1A-B illustrates an example of a hierarchical interplanetarycommunications network or Interplanetary Internet, trained off-line forstatic routing and network planning and for handling gateway selectionaccording to an advantageous embodiment.

FIGS. 2A-B illustrates examples of end-to-end communications for a datapacket arriving at an origination node and being forwarded to adestination node, e.g., earth.

FIG. 3 illustrates an example of cognitive routing for an interplanetarybackbone network of ANN nodes.

FIG. 4 graphically represents communications timing over a link window.

FIG. 5 illustrates an example of pseudo code for determining shortestpath values in time dependent link state routing with each path is takento have sufficient capacity available that capacity does not hampertransmission.

FIG. 6 illustrates an example of generally determining non-zero and timedependent transmission-capacity link cost.

FIG. 7A illustrates an example of generalized-relationship multi-layerperceptron (MLP) neural network analysis for path on-line delayoptimization weight/metric estimation that may be used in computingoptimal or shortest path, for both time dependent link state routing andtime dependent hop-by-hop distance vector optimization.

FIG. 7B illustrates an example of real-time training with cognitiverouting for the interplanetary backbone network.

FIG. 8 illustrates an example of traffic engineering framework for thehierarchical interplanetary communications network.

DETAILED DESCRIPTION

Turning now to the drawings, FIGS. 1A-B illustrate an example of ahierarchical interplanetary communications network 100 or InterplanetaryInternet, trained off-line for static routing and network planningaccording to an advantageous embodiment of the disclosure. Inparticular, a preferred hierarchical interplanetary communicationsnetwork 100 is a self-monitoring ad-hoc network of Artificial NeuralNetwork (ANN) nodes. Each ANN may be in the hardware or software in eachnode. There are several different types of ANN nodes that may besuitable for different types of applications within the network 100.These types include feed-forward ANN nodes that learn with a teacher,recurrent ANN nodes that learn with or without a teacher and selforganizing ANN nodes that learn without a teacher. Most frequently, ANNsare implemented in feed-forward nodes that are based on what is normallyreferred to as a Back Propagation (BP) Algorithm. In general, however,an ANN is relatively robust, simple to train and self-repairing networkand ANN nodes adapt and learn from surrounding (dynamically)environmental conditions exhibiting what is known as a universalapproximation property.

Preferably based on the universal approximation property of multi-layerperceptron (MLP) neural networks, an ANN can do almost anything acomputer does without a priori knowledge of all possible outcomesrequired in a fixed computer program. Thus, ANNs may have application toany computable or recursive function. Such functions may include, butnot limited to for example: tasks that involve prediction/forecasting(e.g., financial forecasting and highway safety tasks); adaptivefiltering and system identification tasks (e.g., adaptive equalizers andfor speech/image recognition); data mining tasks (e.g., data clusteringand fault/failure diagnosis); and, combinatorial optimization tasks(both linear and non-linear). A preferred interplanetary communicationsnetwork 100 is a hierarchical network of ANN nodes trained to use manyof these ANN functions. Preferably, the ANN nodes are trainedsubstantially as described in U.S. patent application Ser. No.11/187,452, (Attorney Docket No. 024.0096 (04-1051)) entitled “TACTICALCOGNITIVE-BASED SIMULATION METHODS AND SYSTEMS FOR COMMUNICATION FAILUREMANAGEMENT IN AD-HOC WIRELESS NETWORKS,” filed Jul. 22, 2005, assignedto the assignee of the disclosure and incorporated herein by reference.

More particularly, a preferred hierarchical interplanetarycommunications network 100 is an Adaptive Self Organizing Neural Network(ASONN) of self monitoring artificial neurons. So, the preferredhierarchical interplanetary communications network 100 may include anumber of ANN element based communications devices, stations or nodesand is referred to herein as an Interplanetary Communications NeuralNetwork (ICNN). Each ICNN node may monitor other directly connected ICNNnodes to identify and predict disconnections and, expectedre-connections. As shown in this example, an ICNN may include spaceborne, airborne and ground based communications units or stations asICNN nodes. Preferably the ICNN nodes area hierarchically organized witha primary system or sub-network (interplanetary backbone network 102);an autonomous intermediate system or sub-network (interplanetaryexternal network 104 with nodes having predictable trajectories); and,one or more tertiary systems or sub-networks (e.g., planetary network(s)106). The autonomous intermediate sub-network and tertiary sub-networksmay periodically attach to the interplanetary backbone network 102.

The interplanetary backbone network 102 originates/terminates at one ormore fixed earth stations 108 and automatically and dynamically routescommunications to gateways in planetary networks 106 according to anadvantageous embodiment. Communications routes may be direct or indirecti.e. through intermediate nodes, such as, passing through geostationary(GEO) satellite 110 in the interplanetary external network 104. Theinterplanetary external network 104 provides shorter-range links throughnodes 110 that have predictable trajectories with a time varyingattachment to the interplanetary backbone network 102. Nodes 110 in theinterplanetary external network 104 may be, for example withoutlimitation, groups of spacecraft in deep space, space craft betweenplanets, sensor node clusters, and groups of space stations.

As shown in more detail in FIG. 1B, the tertiary planetary networks 106may each include a planetary satellite network 112 and planetary surfacenetworks 114-1, 114-2. The planetary satellite network 112 in thisexample, includes a planet stationary satellite 116 (equivalent togeostationary or other high-orbit relay satellite) and low orbitsatellites 118, analogous to low Earth orbit satellites. Furthermore,these networks or sub-networks 102, 104, 106, 112, 114-1, 114-2 may eachoperate based on a different protocol. The sub-networks 102, 104, 106,112, 114-1, 114-2 may use different routing algorithms. Moreover, thesub-networks 102, 104, 106, 112, 114-1, 114-2 may interface with eachother through a designated gateway node (e.g., a planet stationarysatellite 116) in the particular network or sub-network. So, forexample, the planetary satellite network 112 may be based on an highlevel protocol, while planetary surface networks 114-1, 114-2 mayincorporate suitable land mobile wireless ad-hoc routing protocols,modified for energy awareness. A designated gateway planet stationarysatellite 116 may be considered part of the interplanetary backbonenetwork 102 for interplanetary communications. Thus, the planetstationary satellite 116 may be located at Lagrangian points (L₄ or L₅)in the interplanetary backbone network 102 and act as a gateway node forinterplanetary/extraplanetary communications with the particularplanetary surface networks 114-1, 114-2. Nodes in the planetary surfacenetworks 114-1, 114-2 may include any suitable land based or lowaltitude wireless communications capable vehicles. So, for examplewithout limitation, each of the planetary surface networks 114-1, 114-2may include suitable numbers of lander vehicles 120 providing a localnetwork backbone; terrain based craft or rovers 122; flying nodes suchas aircraft 124 (e.g., a piloted and/or drone aircraft) and balloons126; fixed/deployed probes 128 and/or sensors 128, e.g., a sensor array130.

In terrestrial-style networks such as planetary surface networks 114-1,114-2, network latency from propagation delays may be very low and,therefore, might be ignored. Nodes 120-130 in these planetary surfacenetworks 114-1, 114-2 may move erratically and suffer contention fromsharing RF channels. Even with high levels of communications overhead,however, there may be hundreds to thousands of nodes 120-130 thatprovide plentiful capacity for maintaining network connectivity in theplanetary surface networks 114-1, 114-2. Accordingly, planetary surfacenetwork connectivity may be changing continually due to erratic localnode movement. Communications connectivity may be treated withinplanetary surface networks 114-1, 114-2 as equivalent to a suitableearth-based ad-hoc network. Such a suitable Earth-based ad-hoc networkis described in U.S. patent application Ser. No. 11/426,417, (AttorneyDocket No. 05-0278) entitled “NEURAL NETWORK-BASED MOBILITY MANAGEMENTFOR MOBILE AD HOC RADIO NETWORKS,” U.S. patent application Ser. No.11/426,419, (Attorney Docket No. 05-1032) entitled “NEURAL NETWORK-BASEDNODE MOBILITY AND NETWORK CONNECTIVTY PREDECTIONS FOR MOBILE AD HOCRADIO NETWORK,” U.S. patent application Ser. No. 11/426,425, (AttorneyDocket No. 05-1033) entitled “NEURAL NETWORK-BASED MOBILITY MANAGEMENTFOR SELF-PARTITION DETECTION AND IDENTIFICATION OF MOBILE AD HOC RADIONETWORKS,” and U.S. patent application Ser. No. 11/426,428, (AttorneyDocket No. 05-1034) entitled “NEURAL NETWORK-BASED MOBILITY MANAGEMENTFOR HEALING MOBILE AD HOC RADIO NETWORKS,” all four filed Jun. 26, 2006,assigned to the assignee of the present application and incorporatedherein by reference.

By contrast, the interplanetary backbone network 102 may include a muchsmaller number of predictably moving nodes, for example withoutlimitation, tens of nodes 110, 116 or even less, e.g., orbiting nodes116. When connected and/or linked, these backbone nodes 110, 116 form acommunication backbone that may be treated, more or less, as dedicatedpoint-to-point links. However, due to node 116 relative movement, e.g.,orbital motion, the node 110, 116 links are continually changing withmost links occurring only intermittently. Thus, the interplanetarybackbone network 102 is dynamic, albeit on a much coarser time scalethan planetary systems 106 and continuously self-partitioning andself-healing depending upon node 110, 116 positions. Since some nodes116 may have only intermittent availability, all data through thosenodes 116 must be transferred when the node 116 is available, i.e.,during a window of opportunity. This intermittent availability fromlimited and/or intermittent connectivity reduces communicationscapacity, making communications scarce in more extreme cases. Moreover,communications between distant nodes 116 on planets (e.g., Mars) ornon-planets (e.g., Pluto) may be characterized by large propagationdelays, i.e., tens of seconds to tens of minutes or more. Thus, theinterplanetary backbone network 102 may require very different routingprotocols than land based communications to establish and maintaininterplanetary communications. Furthermore, even during a window, whentwo nodes 110, 116 are linked, unless one linked node 110, 116 canhandle the data communications workload required by the other (i.e., thenode 110, 116 has sufficient capacity), transferring data over that linkmay be impractical. Therefore, optimal routing over the interplanetarybackbone network 102 requires knowledge of these constraints to makeefficient use of total available capacity. So, minimizing packet delaysmust be balanced with flow control to maximize network throughput, i.e.,to assure that little if any offered load is rejected and that all ofthe data is transferred in minimum time.

FIGS. 2A-B show examples of end-to-end communications for a data packet140 arriving at an origination node (e.g., 116 in FIGS. 1A-B)represented by transmission window bar 142 and forwarded to adestination node (e.g., earth node 108) represented by transmissionwindow bar 144. In the first example of FIG. 2A, the origination nodeimmediately passes data to an intermediate node (e.g., node 110)represented by transmission window bar 146. Each transmission window,e.g., 148, is designated as a “WINDOW” and undesignated periods 150 areof no communication or blocked, e.g., from planetary eclipses orpropagation conditions between nodes that are too remote.

Earth based network routing has primarily used one of two shortest pathalgorithms, the Bellman-Ford algorithm and the Dijkstra algorithm. TheBellman-Ford algorithm is used in Border Gateway Protocol (BGP) based ona crude measure of average link utilization to minimize congestion. TheDijkstra algorithm is used with Autonomous Systems (AS) based on OpenShortest Path First (OSPF) link costs with link costs configurable by anetwork administrator in a static configuration. Unfortunately, usingthe shortest path as determined, for example, by the minimum number ofhops, ignores the intermittent nature of transmission windows and doesnot account for propagation delays between the nodes or the capacity ofintervening nodes or lack thereof. So, while shortest path routing maywork for fixed static networks or for ad-hoc land based networks, itfails to provide adequate flow control for a much more widely dispersednetwork such as the interplanetary backbone network 102 and further, mayprovide potentially, unstable results and/or oscillations. Additionally,because there may be long periods of time (minutes or hours or days)when an end-to-end connectivity may not exist, traditional routingassumptions are inapplicable to space networks 100.

Space network routes are time-dependent, making their routing metricsmore complex than traditional network routing metrics. Similarlyperformance/routing metrics (e.g., propagation and connectivity) forspace networks are time-dependent. Moreover, improper routing does morethan just slow communications. Improper routing can cause buffercontention and overflow at intermediate nodes that fail to meet longterm storage requirements. This is likely to be a problem especially atdistant nodes, where storage is at a premium or on very short supply.Therefore, optimal routing requires complete knowledge of nodecapacities and of key time-dependent network parameters, such as contacttimes, orbital parameters, traffic loads, and node queuing delays.

In the example of FIG. 2A, the packet 140 arrives at node 142 during awindow to the next node 146. Node 142 immediately forwards the packet tonode 146, where the packet arrives after the transmission propagationdelay between the nodes 142-146. Unfortunately, the packet arrivaloccurs when transmission from node 146 to node 148 is blocked 150 and,so, the packet must be stored (designated STORED 152) on node 146 untilthe next window 154 opens. If, for example, node 146 has insufficientstorage, some of the data in the packet 140 may be lost. Also, ifanother node (not shown) is trying to send data to node 146 (i.e.,contention between the two nodes), that other node may or may not beaware that node 146 is occupied and data is lost from that node. Later,when the next window 154 opens, node 146 passes the packet to thedestination node 148, where the packet arrives after the transmissionpropagation delay between the nodes 146-148. It should be noted thatalthough the time scale employed in the entire system 100,interplanetary backbone 102, or tertiary planetary networks 106 may bebased in standard earth time units (seconds, minutes, hours, days,months, years and etc.,), any suitable time metric may be used, e.g.,the minimum window length.

In the second approach of FIG. 2B, rather than immediately forward thepacket 140 to the first available node, node 142 stores 146 the packetlocally, waiting for an end-to-end connection, or at least a connectionthrough the next node. An end-to-end connection need not necessarilyentail aligned windows, merely that as the packet arrives at one node, awindow opens to the next. So, in this example, the packet 140 waits atnode 142 until a window 158 opens at another intermediate node 160,e.g., another local gateway satellite with negligible node 142 to node160 transmission delay and a direct window from node 160 to thedestination node 144. When the window 158 opens, the windows align withtransmission propagation delay; and, the total end-to-end delay is thelocal storage time 156 plus transmission propagation delay. So,generally, the packet 140 waits at node 142 when the intermediate node144 does not have good link for the next step. This is analogous towaiting to cross all lanes of a 4-lane road until a gap in trafficallows all 4 lanes to be crossed. Generally, this is preferred tocrossing two lanes to end up waiting in a narrow median to cross theother two, only to arrive on the other side at about the same time as ifone had waited for the gap.

FIG. 3 shows an example of cognitive routing for minimized end-to-enddelays in an interplanetary backbone network (e.g., 102) of ANN nodesaccording to an advantageous embodiment. In particular, coarse routingfrom off-line centralized analysis 160 combines in each ANN node withreal time distributed analysis 162 to manage the interplanetary backbonenetwork 102 FIG. 1A for optimal end-to-end packet routing to/from allnetwork nodes. Further, results from real time distributed analysis 162are fed back continually to the off-line centralized analysis 160 tofine tune coarse routing.

Off-line centralized analysis 160 provides cost metric estimations in acentralized pre-computation backbone routing. The backbone routing isbased on contact windows derived from a network description 164 (e.g.,from coarse orbital parameters and initial node positions) and expectedtraffic description 166 (e.g., traffic loads and queuing delays), andfurther based on network time synchronization (i.e., a common view oftime) for efficient routing to generate routing metrics 168. The networkdescription 164 includes, for example, the number of nodes in theinterplanetary backbone network 102, as well as specific characteristicsof each. These specific characteristics may include without limitationorbital parameters, node buffer/storage capacities and associatedstorage costs. The expected traffic description 166 may include theexpected traffic types and data loads. Routing metrics 168 are derivedfrom the network description 164 and the expected traffic description166. The routing metrics 168 are tuned based on distributed results,i.e., results from real time distributed analysis 162. The routingmetrics 168 may include a time varying link description for the entirenetwork, e.g., a window matrix (W(t)). The window matrix for a k nodebackbone network has the form

${W(t)} = \begin{pmatrix}{w_{1}^{1}(t)} & {w_{1}^{2}(t)} & \cdots & {w_{1}^{k}(t)} \\{w_{2}^{1}(t)} & {w_{2}^{2}(t)} & \cdots & {w_{2}^{k}(t)} \\\cdots & \cdots & \cdots & \cdots \\{w_{k}^{1}(t)} & {w_{k}^{2}(t)} & \cdots & {w_{k}^{k}(t)}\end{pmatrix}$

with each matrix entry, w_(j) ^(i)(t), being a time varying binary valueindicating link windows (i.e., expected intermittent linked timeintervals) between pairs of interplanetary backbone network 102 nodes.Since unreachable nodes do not have windows to a current node, onlythose that are reachable for a particular node, i, are in theneighborhood, N_(i), of node i. Also, the routing metrics 168 mayinclude a propagation delay matrix (Δ(t)), having the form

${\Delta (t)} = \begin{pmatrix}{d_{1}^{1}(t)} & {d_{1}^{2}(t)} & \cdots & {d_{1}^{k}(t)} \\{d_{2}^{1}(t)} & {d_{2}^{2}(t)} & \cdots & {d_{2}^{k}(t)} \\\cdots & \cdots & \cdots & \cdots \\{d_{k}^{1}(t)} & {d_{k}^{2}(t)} & \cdots & {d_{k}^{k}(t)}\end{pmatrix}$

with each matrix entry, d_(j) ^(i)(t), indicating link propagation delaybetween pairs of interplanetary backbone network nodes. Modified LinkState Routing—(Modified Dijkstra shortest path algorithms (SPA)) areapplied 170 to the routing metrics 168 to provide time-dependent optimumrouting path and a coarse gateway selection for the interplanetarybackbone network 102.

The real time distributed analysis 162 monitors environmentalconditions, both local environment 172 and global (or macro) environment174, as well as node-switch triggering events 176. The node-switchtriggering events 176 may include any event that is expected to triggerturning to an alternative route, for example without limitation datacongestion (buffer or storage nearing capacity), a link/node failureand/or heavy link utilization. The nodes combine previously learnedbehavior 178 to adapt 180 in real time 182 and fine tune 184 the coarserouting input for specific local needs, e.g., providing fine tunedrouting tables and gateway tables and offering alternate routes asneeded.

FIG. 4 illustrates communications timing over a link window withreference to FIG. 3. Off-line centralized analysis (160) begins withneighbor discovery to identify the neighborhood, N_(i) for each node i.The other ones of the k−1 nodes may be identified as neighbors that cancommunicate with node i for a specified minimum duration, i.e., beyond aplanning horizon T. Typically, the planning horizon T is a long enoughtime to transfer a data packet. Next, contact window lengths aredetermined for each node i to every neighboring (i.e., reachable) node.A packet arriving at a node at a given time (to) 190 between windows192, 194 may be stored/queued locally at the node for some queuing time(t_(q)) 196 until the next contact window 194 occurs. At time (t₀+t_(q))the packet can depart for the neighboring node in the path. Sogenerally, a packet received at node i at t₀ and cached for t_(q) andarriving at node j after a respective propagation delay d_(j) ^(i)(t)198 is given by (t₀+t_(q))+d_(j) ^(i)(t₀+t_(q)):=t₀+D_(j)^(i)(t₀+t_(q)). The optimum (minimum) path delay occurs at some minimumcache time (T) that satisfies: Min_(τ≧0){d_(j) ^(i)(t₀+τ)+τ}=t₀+d_(j)^(i)(t₀+t_(q))=D_(j) ^(i)(t₀), and generally, D_(j)^(i)(t)=min_(τ≧0){d_(j) ^(i)(t₀+τ)+τ} with all values of D_(j) ^(i)(t)being piecewise continuous and finite for all nodes. So, D_(j) ^(i)(t)200 provides the minimum time necessary to relay a packet, i.e., anoptimized queuing cache time (T) for minimized propagation delay (d_(j)^(i)(t)). A shortest path matrix (D(t)) may be generated for the nodeneighborhood with the form

${D(t)} = \begin{pmatrix}{D_{1}^{1}(t)} & {D_{1}^{2}(t)} & \cdots & {D_{1}^{k}(t)} \\{D_{2}^{1}(t)} & {D_{2}^{2}(t)} & \cdots & {D_{2}^{k}(t)} \\\cdots & \cdots & \cdots & \cdots \\{D_{k}^{1}(t)} & {D_{k}^{2}(t)} & \cdots & {D_{k}^{k}(t)}\end{pmatrix}$

that combines waiting time and propagation delay for traversing eachedge (i, j) to guarantee optimally queuing packets for a next contactwindow opportunity.

FIG. 5 illustrates pseudo code for determining offline, time-dependentlink state (modified Dijkstra algorithm) shortest path algorithm usingvalues D_(j) ^(i)(t) to compute and identify the shortest path 200according to an advantageous embodiment. In this example, each path istaken to have sufficient available capacity that link capacity does nothamper transmission, i.e., the time dependent link state for shortestpath routing. Inputs 202 include a source node (s) and starting time(to); a network graph of the backbone with nodes at graph vertices (V)connected by piecewise continuous links or edges (E(t)) and having theform G=(V, E(t₀)); and the matrix D(t). The output 204 (L) is a routingtable indicating the cost of the shortest path to all nodes. In step 206the set of path nodes (P) are initialized (i.e., P is emptied), thedelay entry in L for the source node is set to zero and set to infinity(i.e., disconnected) for all other nodes. Then, beginning in step 208each neighboring node is iteratively selected and added to the path tobegin determining the shortest path to all neighboring nodes. In step210 the minimum route cost delays are iteratively determined from theselected neighboring node to each of the other remaining nodes until therouting table output indicates the cost of the shortest path to allnodes.

Preferably, each node maintains a copy of the routing table (computedoffline) and, whenever a node observes a change in link status withother linked nodes, that node broadcasts only the link changes, e.g.,utilization, queuing, timing synchronization errors. Thus, instead ofeach node broadcasting the whole routing table, nodes only broadcastobserved changes to link status with other neighboring nodes.Furthermore, each node only receives changes from other nodes. Thisreduces the data handling requirements and re-computation necessary forall nodes to refresh for indicated changes for other nodes. While theshortest path matrix (D(t)) quantifies transmission delays between anytwo nodes, those delays may be valid only when the receiving node hascapacity to receive/forward the packet, i.e., ignoring the link cost. Asdescribed hereinabove, during on-line real time operation, ANN nodesadaptively update link weights/metrics.

Frequently the routing tables are cyclical in nature due to theperiodicity of node orbits, i.e., for all i, Table[T_(i)]=Table[T_(i)+η] (the same table) for some fixed integer η≧1. Thetime-dependent routing table will have the form:

(DEST, Delay/Cost, (n₁, . . . , n_(m)))[T_(j)].

Where (n₁, . . . , n_(m)) is the shortest path from node n₁, to noden_(m).

FIG. 6 illustrates an example of time-dependent distance vectorhop-by-hop routing of general determination of individual link cost(l_(j) ^(i)(t)), that is non-zero (e.g., in bits per unit time) and timedependent, with reference to FIG. 4 and with like elements labeledidentically. Table 1 below, shows an example of variables that may beused in hop-by hop routing with corresponding definitions. Moreparticularly, this example shows the application of Bellman optimalityequations to time dependent interplanetary networks and for hop-by-hoprouting.

TABLE 1 C_(i): Storage/Buffering cost of a packet (or data unit) at nodei per unit time Q_(i)(t): Queuing time, at time t at node i, excludingwaiting time to get a window transmission opportunity to node j.U_(ij)(t): Link utilization (between nodes (i, j)) {Note thatQ_(i)(t)~1/(1 − U_(ij)(t)} t_(q): Waiting time to get first transmissionopportunity to node j at time t (this is a function of node location atlime t), t_(q) ≧ 0. Φ_(i)(t): =t_(q) + Q_(i)(t); {Φ_(i)(t)~t_(q) + 1/(1− U_(ij)(t)} N_(i): The set of neighboring nodes of node i. l_(ij)(t):cost of link between nodes (i, j) at time - link cost might be bit rate;generally time dependent. π_(i)(t): Minimum/Shortest Path Cost from nodei to destination node d at time t. D_(ij)(t): Minimum/Optimum WaitingTime at Local Node i to transmit to neighboring node j. τ₀: the valuethat optimizes D_(ij)(t): Min_(τ≧0) {d_(ij)(t + τ) + τ} = t + d_(ij)(t +t_(q)) = D_(ij)(t)

Each node, i, has a time dependent capacity queuing time (Q_(i)(t)) 220that is independent of the time that packets are cached for atransmission window. Further, links from one node, i, to another, j,have utilization rate (U_(j) ^(i)(t)) that is roughly inversely relatedto this queuing time, i.e., Q_(i)(t)˜(1−U_(j) ^(i)(t))⁻¹. So, a packetarriving at node, i, pauses for a total queuing time (Φ_(i)(t)) 222 thatis defined by the sum of the capacity queuing time (Q_(i)(t)) 220 andthe window queuing time (t_(q)) 196. So, more specifically,

Φ_(i)(t):=t_(q)+Q_(i)(t); Φ_(i)(t)˜t_(q)+(1−U_(j) ^(i)(t))⁻¹. From this,a time dependent minimum/shortest path cost (π_(i)(t)) may be determinedfor each neighboring node by balancing the delay for waiting for anoptimum window against waiting for optimum path cost.

In a quick and simple optimization using the Bellman optimalityequations, e.g., an off-line coarse optimization in each node, storagecosts (C_(i)) and individual link costs (l_(j) ^(i)(t)) are taken to beidentical and constant for all nodes, i.e., C_(i)=Constant, j=1, 2, . .. k; and l_(j) ^(i)(t))=Constant for all t. Further, queuing traffic dueto loads is taken to be negligible (i.e. Q_(i)(t)˜0). Then, recursiveanalysis is applied be using state of the art dynamic programmingmethods to solve Bellman optimality equations:π_(i)(t)=Min[t_(q)+π_(i)(t_(q)); min {D_(j) ^(i)(t)+π_(i)(D_(j)^(i)(t))}], π_(d)=0 (i.e., optimum path to node d to itself is zero),where t_(q)+π_(i)(t_(q)) is the result of only waiting at node i to getfirst contact window to node to j; and {D_(j) ^(i)(t)+π_(i)(D_(j)^(i)(t))} provides an optimal wait at node i, i.e., recursively solvedcan and is based on a.

Table 2 shows an example of a hop-by-hop routing table maintained ateach node; For M network nodes, there are a total of M−1 such tables ateach network node. Each routing table is am array indexed by discretetime intervals, T₀, . . . T_(i). The time intervals are formed bydividing the time horizon into sufficiently small intervals: [T_(i),T_(i)+1], i=1, 2, L. Preferably the time intervals are small enough tofix the propagation delay within each time horizon interval.

TABLE 2 Time- cyclic One Table per destination node ∩ TIME COST/DELAYNEXT-HOP ↑↓ T₁ P(1) Node-1 ↑↓ T₂ P(2) Node-2 ↑↓ : : : ↑↓ : : : ∪ T_(K)P(k) Node-k

Frequently the routing tables, such as shown in the example of Table 2,are cyclical in nature due to the periodicity of node orbits, i.e., forall i, Table[T_(i)]=Table [T_(i)+η] (the same table) for some fixedinteger η≧1. Next hop nodes for the shortest route between two nodes, n₁and n_(m), selected by link state determined by:

(DEST, Delay/Cost, next-hop)[T_(j)].

This simple optimization may be determined independently by each node,where routing tables may be updated and, the result may also be used forcoping with operational link/node failures. Normally, on-line trainingis conducted before the nodes are launched into space and in spacetraining is off-line. Furthermore, much of the training can be done in amainframe operating as an ANN, uploaded to a particular node and loadedwith a reboot, for example. Also, traffic primarily flowing to and fromEarth may present a special case with time windows that are differentfor inbound and outbound traffic. Inbound and outbound can then beseparately scheduled and configured to reduce computational complexity.

Further, a more rigorous optimization that is not deterministic may bederived in a general form of the path optimization relationship, withvalues determined on-line in recursive analysis during routingconvergence analysis. This generalized relationship of this morerigorous optimization has the form,π_(i)(t)=Min[Φ_(i)(t)C_(i)+π_(i)(Φ_(i)(t)); min{(l_(j)^(i)+(Φ_(i)(t)+τ)C_(i)+π_(i)(D_(j) ^(i)(t))}], whereΦ_(i)(t)C_(i)+π_(i)(Φ_(i)(t)) is the result of waiting at node i to geta first contact window to node to j; and extended by queuing time addeddue to traffic load; and {(l_(j) ^(i)+(Φ_(i)(t)+τ)C_(i)+π_(i)(D_(j)^(i)(t))} provides an optimal wait at node i. For a discreterepresentation, rather than based on earth time units as describedabove, the time horizon is divided into sufficiently small intervals([T_(h), T_(h+1)] for h=1, 2, . . . , H), and t belongs to [T_(i),T_(i+1)]. Preferably, the length of the time interval is selectedaccording to node orbital parameters. It should be noted that π_(i)(t)is always based on historic data and, therefore, may always be out ofdate with respect to actual current traffic and delays. However, thismay be mitigated through adequately training ANN nodes toforecast/predict expected traffic for any future time. Theseforecasts/predictions estimate π_(j)(D_(j) ^(i)(t)) as well as linkutilization/traffic load at time D_(j) ^(i)(t) for all neighboringnodes, i.e., Φ_(j)(D_(j) ^(i)(t)) and U_(j) ^(i)(D_(j) ^(i)(t)) for allnodes in neighborhood N_(i).

FIG. 7A illustrates an example of a MLP ANN for path on-line delayoptimization of the generalized Bellman optimality relationship forπ_(i)(t), according to an advantageous embodiment. This MLP neural net230 example includes an input layer 232, an output layer 234 and ahidden intermediate layer 236. Time varying originating and destinationorbital parameters and node positions are provided to the input layer232. ANN nodes in the hidden layer 236 have a monotonically increasingnonlinear transfer function. Provided there are a sufficient number ofhidden nodes in the hidden layer 236, the MLP neural net 230 filtersinput estimation errors such that the hidden layer 236 through theuniversal approximation property of ANN nodes can approximate anyarbitrary (nonlinear) function to a desired degree of accuracy.Alternately, ANN nodes can process the universal approximation propertythrough what is known as the radial basis function (RBF). Thus, in thisexample, the output layer 234 provides improved estimates for totalqueuing time Φ_(j) ^(i)(t) 222 and shortest path matrix D_(j) ^(i)(t)200 by filtering the inputs through the universal approximation propertyof ANN nodes in the hidden layer 236.

FIG. 7B illustrates an example of real-time training for the MLP neuralnet 230 of FIG. 7A with cognitive routing for the interplanetarybackbone network according to FIG. 3. The off-line centralized analysis160 provides coarse routing in ANN off-line training 242 to the MLPneural net 230. Based on that ANN off-line training 240 the MLP neuralnet 230 predicts the matrix D_(j) ^(i)(t) 242 from a node (i) in theinput layer 232 to a node (j) in the output layer 234. A measured matrixD_(j) ^(i)(t) 244, collected with node-switch triggering events 176, isprovided for comparison against the predicted matrix D_(j) ^(i)(t) 242to generate an error signal 246 indicating the difference between thepredicted and measured values 242, 244. Based on this error signal 246the MLP neural net 230 adjusts the waiting times and other values in thematrix D_(j) ^(i)(t) transfer function for each node, learning andadapting 248 to reduce/eliminate error signal 246.

Thus, each node includes a link path table for each destination node ina cyclic set of tables/entries of 3-tuples, i.e., (T_(i),Delay_(i)/Cost_(i), Next Hop_(i)), i=1, 2 . . . k−1, as shown in Table 2hereinabove. These routing tables, many cases, are cyclic in nature dueto the periodicity of network node orbits

Preferably, each node continuously runs a link metric predictionalgorithm or, alternately, the link metric prediction algorithm may berun at discrete time intervals. Whenever the error signal exceeds apre-selected threshold, the values may be automatically updated toimprove predicted/estimated routes to all potential destinations, i.e.,for a smaller cost/delay to neighboring nodes. Thereafter, existingroutes are continually refreshed and, any that fail to refresh (e.g.,time out) may be deleted. Although this example is shown for matrixD_(j) ^(i)(t) prediction analysis and tuning, this same example of FIG.7B may be used for predicting total queuing time Φ_(j) ^(i)(t) and pathtimes π_(i)(D_(j) ^(i)(t)) and π_(i)(Φ_(j) ^(i)(t)) as well aspredicting utilization rate (U_(j) ^(i)(t)). It should be noted thatbecause the prediction time span is on the order of the network diameter(i.e., the distance between the two most distant connected or end-to-endnodes) predicted value accuracy may be low.

Occasionally, sending nodes may detect link or node failures because,for example, a neighbor node has moved too far away or lost power. Suchan occurrence may be noticed, for example, when unicast transmissionsfail (i.e., transport layer failures) or when some predefined time outperiod expires without an expected “hello” return message. Such alink/node failure may open a path from the sending node, making one ormore destinations unreachable for some period of time. When the sendingnode has other next hop alternatives to the same destination, thesending node just updates its routing table and sends a notification ofthe update to its neighbors. If, however, the only path from the sendingnode to the destination was through the failed link/node, the networkhas been unexpectedly partitioned and no path is available to thedestination. If the network has a central network management system(NMS) that is equipped to detect partitions, then the sending node musthold the data for some period of time for the central NMS to repair thefaulty link/node. Otherwise, when a link or node failure occurs, forexample, some other approach is required to route packets.

If accurate cost metric/weight estimates/predictions are unavailable,then agent-based or neurodynamic programming-based routing may be used.Agent-based routing uses on-line network observations and does notrequire a complete network model, e.g., a priori knowledge of linkcosts, node capacities, or traffic demands. Further, agent-based routingoperates somewhat independently of a single centralized controller andhas the capacity to adapt autonomously to changes in network parametersand traffic demands.

FIG. 8 illustrates an example of traffic engineering framework 250 foroptimizing traffic in a hierarchical interplanetary communicationsnetwork (e.g., 100 of FIGS. 1A-B) according to an advantageousembodiment. A description 252 of the backbone network is provided foroptimization 254 of a routing model 256, e.g., using an implementationof time-dependent link state or a time-dependent distance vector (DV) asdescribed hereinabove. Traffic demand forecasts 258 are also provided tooptimization 254 and the routing model 256. The routing model 256generates weight settings and distribution 260 that are passed tonetwork routers for network operation 262. During network operation 262the network measures 264 traffic loads, network delays and any otherrelevant parameters, which are passed on to network nodes for off-lineanalysis and management.

Advantageously, a backbone network in a preferred interplanetarycommunications network provides robust end-to-end network routing. Apreferred interplanetary backbone network uses link state and distancevector routing for on-line automatic dynamic routing. End-to-endtransmission times may be minimized and link cost might be dramaticallyreduced. Moreover, the backbone network is dynamically adaptable tonetwork changes and, further, may be immune to many networkpartitions/node failures. Separating nodes may be identified in advanceand reconnection (i.e., communications windows) predicted according toorbital motion. Network stability is improved with potential networkcommunications oscillation reduced. Alternate routes may be identifiedwith relatively fast convergence in both off-line and on-lineimplementations.

While the embodiments of the disclosure have has been described in termsof preferred embodiments, those skilled in the art will recognize thatthe embodiments can be practiced with modification within the spirit andscope of the appended claims. It is intended that all such variationsand modifications fall within the scope of the appended claims. Examplesand drawings are, accordingly, to be regarded as illustrative ratherthan restrictive.

1. A method of managing an interplanetary communications network, saidmethod comprising the steps of: a) providing a backbone networkconfiguration, said backbone network configuration having a plurality(k) of nodes; b) determining transmission propagation times from eachnode to every other of said plurality of nodes; c) determiningconnection windows from said each node to every other node of saidplurality of nodes; and d) determining an optimum transmission time ineach said connection window to a respective one of said plurality ofnodes.
 2. A method as in claim 1, wherein the step (c) of determiningconnection windows identifies re-occurring windows to one or more ofsaid plurality of nodes.
 3. A method as in claim 2, wherein the step (c)of determining connection windows provides a window matrix indicatingwindows occurring between said each node and said every other node andhas the form ${W(t)} = {\begin{pmatrix}{w_{1}^{1}(t)} & {w_{1}^{2}(t)} & \cdots & {w_{1}^{k}(t)} \\{w_{2}^{1}(t)} & {w_{2}^{2}(t)} & \cdots & {w_{2}^{k}(t)} \\\cdots & \cdots & \cdots & \cdots \\{w_{k}^{1}(t)} & {w_{k}^{2}(t)} & \cdots & {w_{k}^{k}(t)}\end{pmatrix}.}$
 4. A method as in claim 3, wherein matrix entries,w_(j) ^(i)(t), comprise time varying binary values indicating linkedperiods from node i to each of the other (k−1) nodes j and only entriesin row i with said time varying binary values are in a neighborhood ofnode i.
 5. A method as in claim 3, wherein the transmission propagationtimes determined in step (b) are time varying.
 6. A method as in claim5, wherein the step (b) of determining transmission propagation timesprovides a propagation delay matrix indicating time varying propagationdelays between said each node and said every other node and has the form${\Delta (t)} = {\begin{pmatrix}{d_{1}^{1}(t)} & {d_{1}^{2}(t)} & \cdots & {d_{1}^{k}(t)} \\{d_{2}^{1}(t)} & {d_{2}^{2}(t)} & \cdots & {d_{2}^{k}(t)} \\\cdots & \cdots & \cdots & \cdots \\{d_{k}^{1}(t)} & {d_{k}^{2}(t)} & \cdots & {d_{k}^{k}(t)}\end{pmatrix}.}$
 7. A method as in claim 6, wherein the step (d) ofdetermining optimum transmission times comprises generating a matrixhaving the form ${D(t)} = {\begin{pmatrix}{D_{1}^{1}(t)} & {D_{1}^{2}(t)} & \cdots & {D_{1}^{k}(t)} \\{D_{2}^{1}(t)} & {D_{2}^{2}(t)} & \cdots & {D_{2}^{k}(t)} \\\cdots & \cdots & \cdots & \cdots \\{D_{k}^{1}(t)} & {D_{k}^{2}(t)} & \cdots & {D_{k}^{k}(t)}\end{pmatrix}.}$
 8. A method as in claim 7, wherein the step (d) ofdetermining optimum transmission times further comprises determining astorage time (τ) above a minimum queuing time (t_(q)) for storing apacket arriving at said first node at a given arrival time (t₀) in saideach window until said packet is forwarded to said respective one.
 9. Amethod as in claim 8, wherein entries, D_(j) ^(i)(t), in the matrixsatisfy: Min_(τ≧0){d_(j) ^(i)(t₀+τ)+τ}=t₀+d_(j) ^(i)(t₀+t_(q))=D_(j)^(i)(t₀).
 10. A method as in claim 9, wherein all values of D_(j)^(i)(t) are piecewise continuous and finite for all nodes and D_(j)^(i)(t)=min_(τ≧0){d_(j) ^(i)(t₀+τ)+τ.
 11. A method as in claim 8, beforethe step (d) of determining optimum transmission times, said methodfurther comprising determining a time dependent minimum/shortest pathcost (π_(i)(t)) from each node i to each other node j.
 12. A method asin claim 11, wherein determining said time dependent minimum/shortestpath cost comprises recursively analyzingπ_(i)(t)=Min[t_(q)+π_(i)(t_(q)); min {D_(j) ^(i)(t)+π_(i)(D_(j)^(i)(t))}].
 13. A method as in claim 12, wherein t_(q)+π_(i)(t_(q)) isthe result of only waiting at node i to get first contact window to nodeto j; and {D_(j) ^(i)(t)+π_(i)(D_(j) ^(i)(t))} provides an optimal waittime at node i.
 14. A method as in claim 11, further comprisingdetermining storage costs (C_(i)) for each node i, a link cost (l_(j)^(i)(t)) for each node j linked to i, a link utilization rate U_(j)^(i)(t) and a corresponding node capacity Q_(i)(t).
 15. A method as inclaim 14, further comprising determining total queuing time (Φ_(i)(t))for each node i, where Φ_(i)(t):=t_(q)+Q_(i)(t).
 16. A method as inclaim 15, wherein determining said time dependent minimum/shortest pathcost comprises recursively analyzingπ_(i)(t)=Min[Φ_(i)(t)C_(i)+π_(i)(Φ_(i)(t)); min{(l_(j)^(i)+(Φ_(i)(t)+τ)C_(i)+π_(i)(D_(j) ^(i)(t))}].
 17. A method as in claim16, wherein Φ_(i)(t)C_(i)+π_(i)(Φ_(i)(t)) is the result of waiting atnode i to get first contact window to node to j extended by traffic loadqueuing time; and (l_(j) ^(i)+(Φ_(i)(t)+τ)C_(i)π_(i)(D_(j) ^(i)(t))}provides an optimal wait time at node i.
 18. A method as in claim 16,wherein multi-layer perceptron (MLP) ANN analysis is used forrecursively analyzing π_(i)(t).
 19. A method as in claim 18, wherein aresult from said MLP ANN analysis is compared with a measured delay andan error signal from comparing said measured delay is fed back to adaptsaid MLP ANN analysis.
 20. A method as in claim 18, wherein each node iincludes a link path table for each destination node j.
 21. A method asin claim 20, wherein said link path table is in a cyclic 3-tuple set.22. A method as in claim 6, wherein the complete shortest path and theassociated costs between a given node and all other nodes in the networkis determined from D(t) at a selected time.
 23. A method as in claim 2,wherein the re-occurring windows reoccur periodically to one or more ofsaid plurality of nodes and ones of said plurality of nodes receiving amajority of communications from Earth nodes having a separate scheduleand configuration for sending communications to Earth and for receivingcommunications from Earth.
 24. A method as in claim 1, wherein wheneverone of said plurality of nodes determines a neighboring node isunresponsive, said node uses agent based routing to reroute paths aroundsaid unresponsive neighboring node.
 25. An Artificial Neural Network(ANN) node for a backbone network in an interplanetary communicationsnetwork, said backbone network including a plurality (k) of nodes, saidANN node comprising: a window matrix indicating re-occurringcommunications windows between each node and each other of saidplurality of nodes; a propagation delay matrix indicating time varyingtransmission propagation times from each node to said each other of saidplurality of nodes; and means for determining an optimum transmissiontime in each window to a respective one of said plurality of nodes. 26.An ANN node as in claim 25, wherein said window matrix is a k by kmatrix with one row for each one of said plurality of nodes, each entry,w_(j) ^(i)(t), in each row indicating communications windows from acorresponding node i to each node j of the other (k−1) nodes in saidbackbone network, said window matrix having the form${W(t)} = {\begin{pmatrix}{w_{1}^{1}(t)} & {w_{1}^{2}(t)} & \cdots & {w_{1}^{k}(t)} \\{w_{2}^{1}(t)} & {w_{2}^{2}(t)} & \cdots & {w_{2}^{k}(t)} \\\cdots & \cdots & \cdots & \cdots \\{w_{k}^{1}(t)} & {w_{k}^{2}(t)} & \cdots & {w_{k}^{k}(t)}\end{pmatrix}.}$
 27. An ANN node as in claim 26, wherein each windowmatrix entry comprises a time varying binary value indicating linkedperiods and only entries in row i with said time varying binary valuesare in a neighborhood Ni of node i.
 28. An ANN node as in claim 27,wherein the propagation delay matrix has the${\Delta (t)} = {\begin{pmatrix}{d_{1}^{1}(t)} & {d_{1}^{2}(t)} & \cdots & {d_{1}^{k}(t)} \\{d_{2}^{1}(t)} & {d_{2}^{2}(t)} & \cdots & {d_{2}^{k}(t)} \\\cdots & \cdots & \cdots & \cdots \\{d_{k}^{1}(t)} & {d_{k}^{2}(t)} & \cdots & {d_{k}^{k}(t)}\end{pmatrix}.}$
 29. An ANN node as in claim 28, wherein the means fordetermining optimum transmission times comprises generating a matrixhaving the form ${D(t)} = {\begin{pmatrix}{D_{1}^{1}(t)} & {D_{1}^{2}(t)} & \cdots & {D_{1}^{k}(t)} \\{D_{2}^{1}(t)} & {D_{2}^{2}(t)} & \cdots & {D_{2}^{k}(t)} \\\cdots & \cdots & \cdots & \cdots \\{D_{k}^{1}(t)} & {D_{k}^{2}(t)} & \cdots & {D_{k}^{k}(t)}\end{pmatrix}.}$
 30. An ANN node as in claim 29, wherein said means fordetermining optimum transmission times comprises means for determining astorage time (τ) above a minimum queuing time (t_(q)) for storing apacket arriving in said each window at said node i at a given arrivaltime (t₀) until said packet is forwarded to said respective node j. 31.An ANN node as in claim 30, wherein said means for determining optimumtransmission times determines matrix entries, D_(j) ^(i)(t), accordingto the relationship Min_(τ≧0) {d_(j) ^(i)(t₀+τ)+τ}=t₀+d_(j)^(i)(t₀+t_(q))=D_(j) ^(i)(t₀).
 32. An ANN node as in claim 31, whereinall values of D_(j) ^(i)(t) are piecewise continuous and finite for allnodes and D_(j) ^(i)(t)=min_(τ≧0){d_(j) ^(i)(t₀+τ)+τ}.
 33. An ANN nodeas in claim 30, further comprising means for determining a timedependent minimum/shortest path cost (π_(i)(t)) from each node i to eachneighboring node j.
 34. An ANN node as in claim 33, wherein said meansfor determining said time dependent minimum/shortest path costrecursively analyzesπ_(i)(t)=Min[t _(q)+π_(i)(t _(q)); min{D _(j) ^(i)(t)+π_(i)(D _(j)^(i)(t))}], wherein t_(q)+π_(i)(t_(q)) is the result of only waiting atnode i to get first contact window to node to j; and {D_(j)^(i)(t)+π_(i)(D_(j) ^(i)(t))} provides an optimal wait time at node i.35. An ANN node as in claim 33, wherein said means for determining saidtime dependent minimum/shortest path cost further determines storagecosts (C_(i)) for each node i, a link cost (l_(i) ^(j)(t)) for each nodej linked to i, a link utilization rate U_(j) ^(i)(t) and a correspondingnode capacity Q_(i)(t).
 36. An ANN node as in claim 35, furthercomprising means for determining total queuing time (Φ_(i)(t)) for eachnode i, where Φ_(i)(t):=t_(q)+Q_(i)(t).
 37. An ANN node as in claim 36,wherein said means for determining said time dependent minimum/shortestpath cost recursively analyzesπ_(i)(t)=Min[Φ_(i)(t)C _(i)+π_(i)(Φ_(i)(t)); min{(l_(j)^(i)+(Φ_(i)(t)+τ)C _(i)+π_(i)(D _(j) ^(i)(t))}], whereinΦ_(i)(t)C_(i)+π_(i)(Φ_(i)(t)) is the result of waiting at node i to getfirst contact window to node to j extended by traffic load queuing time;and {(l_(j) ^(i)+(Φ_(i)(t)+τ)C_(i)+π_(i)(D_(j) ^(i)(t))} provides anoptimal wait time at node i.
 38. An interplanetary communicationsnetwork, said interplanetary communications network having a backbonenetwork comprising k ANN nodes as in claim
 37. 39. An interplanetarycommunications network as in claim 38, further comprising means forrecursively applying multi-layer perceptron (MLP) ANN analysis toπ_(i)(t).
 40. An interplanetary communications network as in claim 39,further comprising means for comparing a result from said MLP ANNanalysis with a measured delay, said means for comparing generating anerror signal responsive to a difference between said measured delay andsaid result.
 41. An ANN node as in claim 25, wherein ones of saidre-occurring communications windows reoccur periodically and said ANNnode further comprises agent based routing means for rerouting pathsaround an unresponsive neighboring said node.
 42. A method of managing abackbone network of an interplanetary communications network, saidmethod comprising the steps of: a) providing a backbone networkconfiguration, said backbone network configuration having a plurality ofnodes; b) determining transmission propagation times from each node toevery other of said plurality of nodes; c) determining connectionwindows from said each node to every other node of said plurality ofnodes, ones of said connection windows being re-occurring windows; andd) determining an optimum transmission time in each said connectionwindow to a respective one of said plurality of nodes.
 43. A method asin claim 42, wherein the step (c) of determining connection windowsprovides a window matrix indicating windows occurring between said eachnode and said every other node.
 44. A method as in claim 43, whereinmatrix entries comprise time varying binary values indicating linkedperiods from a first node to each other said plurality of nodes, ones ofplurality of nodes being in a neighborhood of at least one other node.45. A method as in claim 43, wherein the transmission propagation timesdetermined in step (b) are time varying.
 46. A method as in claim 45,wherein the step (b) of determining transmission propagation timesprovides a propagation delay matrix indicating time varying propagationdelays between said each node and said every other node.
 47. A method asin claim 46, wherein the step (d) of determining optimum transmissiontimes comprises generating a path matrix.
 48. A method as in claim 47,wherein the step (d) of determining optimum transmission times furthercomprises determining a storage time above a minimum queuing time forstoring a packet arriving at said first node at a given arrival time insaid each window until said packet is forwarded to said respective one.49. A method as in claim 48, before the step (d) of determining optimumtransmission times, said method further comprising determining a timedependent minimum/shortest path cost from said each node to each othernode.
 50. A method as in claim 49, wherein determining said timedependent minimum/shortest path cost comprises recursively analyzingpaths to determine an optimal wait time at said each node.
 51. A methodas in claim 49, further comprising determining storage costs for saideach node, a link cost for each pair of linked nodes, a link utilizationrate for each ling and a corresponding node capacity.
 52. A method as inclaim 51, further comprising determining total queuing time for eachnode.
 53. A method as in claim 52, wherein determining said timedependent minimum/shortest path cost comprises recursively analyzing theresult of waiting at node i to get first contact window to node to jextended by traffic load queuing time to provides an optimal wait timeat said each node.
 54. A method as in claim 53, wherein multi-layerperceptron (MLP) ANN analysis is used for recursively analyzing.
 55. Amethod as in claim 54, wherein a result from said MLP ANN analysis iscompared with a measured delay and an error signal from comparing saidmeasured delay is fed back to adapt said MLP ANN analysis.
 56. A methodas in claim 54, wherein each node includes a link path table for eachdestination node in a cyclic 3-tuple set.
 57. A method as in claim 42,wherein the re-occurring windows reoccur periodically to one or more ofsaid plurality of nodes.
 58. A method as in claim 42, wherein wheneverone of said plurality of nodes determines a neighboring node isunresponsive, said node uses agent based routing to reroute paths aroundsaid unresponsive neighboring node.
 59. A method as in claim 42, whereinones of said plurality of nodes receiving a majority of communicationsfrom Earth nodes have a separate schedule and configuration for sendingcommunications to Earth and for receiving communications from Earth.